The field of data analytics has grown tremendously in recent years. With an ever-increasing proliferation of affordable technologies capable of collecting and reporting data, coupled with an also ever-increasing ability to process data cheaply, the role that data and data analysis plays in identifying trends and improving decision-making processes is also growing both in use and importance to many facets of industry, commerce, and research. Sophisticated data analytics are commonly used in a broad range of fields such as marketing, insurance, telecommunications, healthcare, and pharmaceuticals. Typically, the desired result of these efforts is a predictive tool that serves the primary interest at hand, such as a well-formed question or specific target of study. Often, it is left to researchers or users to decide when and how to apply what is learned. In that sense, analytics systems are typically disconnected from the processes and systems they study, since they are not generally disposed to automatically acting on the analyses they perform. This is particularly the case when analytics systems aggregating multiple data streams are applied to systems operating in the physical world. In general, analytics systems have not been employed as administrative tools in which the products of their evaluation processes would be automatically applied to the system or systems under study.